import os

import matplotlib.pyplot as plt
import torch
from d2l import torch as d2l

# 9.5.1. 下载和预处理数据集
# @save
d2l.DATA_HUB['fra-eng'] = (d2l.DATA_URL + 'fra-eng.zip',
                           '94646ad1522d915e7b0f9296181140edcf86a4f5')


# @save
def read_data_nmt():
    """载入“英语－法语”数据集"""
    data_dir = d2l.download_extract('fra-eng')
    with open(os.path.join(data_dir, 'fra.txt'), 'r',
              encoding='utf-8') as f:
        return f.read()


raw_text = read_data_nmt()
print(raw_text[:200])


# @save
def preprocess_nmt(text):
    """预处理“英语－法语”数据集"""

    def no_space(char, prev_char):
        return char in set(',.!?') and prev_char != ' '

    # 使用空格替换不间断空格
    # 使用小写字母替换大写字母
    text = text.replace('\u202f', ' ').replace('\xa0', ' ').lower()
    # 在单词和标点符号之间插入空格
    out = [' ' + char if i > 0 and no_space(char, text[i - 1]) else char
           for i, char in enumerate(text)]
    return ''.join(out)


text = preprocess_nmt(raw_text)
# 通过一下打印说明text是一个非常长的字符串包含了英文和法语的句子
print(len(text))
for i in text:
    print(i)
    break

# 每一行一个英文和发文句子组成的字符串 例如 come in .	entrez !
print(text.split('\n')[100])


# 9.5.2. 词元化
def tokenize_nmt(text, num_examples=None):
    """词元化“英语－法语”数据数据集"""
    source, target = [], []
    for i, line in enumerate(text.split('\n')):
        if num_examples and i > num_examples:
            break
        parts = line.split('\t')
        if len(parts) == 2:
            source.append(parts[0].split(' '))
            target.append(parts[1].split(' '))
    return source, target


source, target = tokenize_nmt(text)
# 通过长文本使用\n进行切分，然后将每一行按照 \t切分出英文和发文，然后存到两个不同的列表中。这两个列表长度应该相等
print(len(source), len(target))


# 统计16万条文字信息，对应英文和法文，发现大部分的句子词元少于20个。
def show_list_len_pair_hist(legend, xlabel, ylabel, xlist, ylist):
    """绘制列表长度对的直方图"""
    d2l.set_figsize()
    _, _, patches = d2l.plt.hist(
        [[len(l) for l in xlist], [len(l) for l in ylist]])
    d2l.plt.xlabel(xlabel)
    d2l.plt.ylabel(ylabel)
    for patch in patches[1].patches:
        patch.set_hatch('/')
    d2l.plt.legend(legend)


show_list_len_pair_hist(['source', 'target'], '# tokens per sequence',
                        'count', source, target);
plt.show()

# 9.5.3. 词表
src_vocab = d2l.Vocab(source, min_freq=2,
                      reserved_tokens=['<pad>', '<bos>', '<eos>'])
print("特殊词信息", len(src_vocab))
print(src_vocab['<pad>'], src_vocab['<bos>'], src_vocab['<eos>'], src_vocab.to_tokens(0), src_vocab.to_tokens(4),
      src_vocab.to_tokens(5))


# 9.5.4. 加载数据集
def truncate_pad(line, num_steps, padding_token):
    """截断或填充文本序列"""
    if len(line) > num_steps:
        return line[:num_steps]  # 截断
    return line + [padding_token] * (num_steps - len(line))  # 填充


print(truncate_pad(src_vocab[source[0]], 10, src_vocab['<pad>']))


# print(truncate_pad(src_vocab[source[0]], 10, src_vocab['<unk>']))
#
def build_array_nmt(lines, vocab, num_steps):
    """将机器翻译的文本序列转换成小批量"""
    lines = [vocab[l] for l in lines]
    lines = [l + [vocab['<eos>']] for l in lines]
    array = torch.tensor([truncate_pad(
        l, num_steps, vocab['<pad>']) for l in lines])
    valid_len = (array != vocab['<pad>']).type(torch.int32).sum(1)
    return array, valid_len


tgt_vocab = d2l.Vocab(target, min_freq=2,
                      reserved_tokens=['<pad>', '<bos>', '<eos>'])

src_array, src_valid_len = build_array_nmt(source, src_vocab, 100)
tgt_array, tgt_valid_len = build_array_nmt(target, tgt_vocab, 100)
print(src_array, src_valid_len)
print(tgt_array, tgt_valid_len)


# 9.5.5. 训练模型
def load_data_nmt(batch_size, num_steps, num_examples=600):
    """返回翻译数据集的迭代器和词表"""
    text = preprocess_nmt(read_data_nmt())
    source, target = tokenize_nmt(text, num_examples)
    src_vocab = d2l.Vocab(source, min_freq=2,
                          reserved_tokens=['<pad>', '<bos>', '<eos>'])
    tgt_vocab = d2l.Vocab(target, min_freq=2,
                          reserved_tokens=['<pad>', '<bos>', '<eos>'])
    src_array, src_valid_len = build_array_nmt(source, src_vocab, num_steps)
    tgt_array, tgt_valid_len = build_array_nmt(target, tgt_vocab, num_steps)
    data_arrays = (src_array, src_valid_len, tgt_array, tgt_valid_len)
    data_iter = d2l.load_array(data_arrays, batch_size)
    return data_iter, src_vocab, tgt_vocab


train_iter, src_vocab, tgt_vocab = load_data_nmt(batch_size=2, num_steps=8)
for X, X_valid_len, Y, Y_valid_len in train_iter:
    print('X:', X.type(torch.int32))
    print('X的有效长度:', X_valid_len)
    print('Y:', Y.type(torch.int32))
    print('Y的有效长度:', Y_valid_len)
    break
"""
X: tensor([[ 7, 35,  4,  3,  1,  1,  1,  1],
        [ 0,  4, 70,  5,  3,  1,  1,  1]], dtype=torch.int32)
X的有效长度: tensor([4, 5])
Y: tensor([[ 6,  7, 85,  4,  3,  1,  1,  1],
        [19,  0,  5, 54,  5,  3,  1,  1]], dtype=torch.int32)
Y的有效长度: tensor([5, 6])
"""
